Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
#data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f9d49c550b8>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f9d49b4e198>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data, learning rate)

In [26]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(dtype = tf.float32, shape = (None, image_width, image_height, image_channels), name = "input_real")
    input_z = tf.placeholder(dtype = tf.float32,shape = (None, z_dim), name = "input_z")
    lr = tf.placeholder(dtype = tf.float32, shape=(None), name = 'learning_rate')
    return input_real, input_z, lr


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False, alpha = 0.2):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    with tf.variable_scope('discriminator', reuse=reuse):
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        relu1 = tf.maximum(alpha * x1, x1)
        # 16x16x64
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        # 8x8x128
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        # 4x4x256

        # Flatten it
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    reuse = not is_train
    rate=0.2 #dropout rate
    with tf.variable_scope('generator', reuse = reuse):
        # First fully connected layer
        x1 = tf.layers.dense(z, 7*7*512)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(0.2 * x1, x1)
        
    
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer(), activation = None)
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(0.2 * x2, x2)
        x2 = tf.layers.dropout(x2, rate, training=is_train)
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=1, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer(), activation = None)
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(0.2 * x3, x3)
        x3 = tf.layers.dropout(x3, rate, training=is_train)
 
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer(),  activation = None)
        
        
        out = tf.tanh(logits)
        
        return out




"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    smooth = 0.1
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*(1.0 - smooth)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [15]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [28]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    #saver = tf.train.Saver()
    losses = []
    samples = []
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
        
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
        
    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)  #learning_rate or lr?
    
    steps = 0
    
    print_every = 20
    show_every = 50
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                steps += 1
                batch_images*=2.0
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images,input_z: batch_z, lr:learning_rate})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr:learning_rate})

                if steps % print_every == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    # Save losses to view after training
                    
                    losses.append((train_loss_d, train_loss_g))

                if steps % show_every == 0:
                    show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)

        #saver.save(sess, './checkpoints/generator.ckpt')

    #with open('samples.pkl', 'wb') as f:
    #    pkl.dump(samples, f)
    
    return losses, samples    
    

                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [29]:
batch_size = 64
z_dim = 100
learning_rate = 1e-3
beta1 = 0.5

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 1.4862... Generator Loss: 7.6041
Epoch 1/2... Discriminator Loss: 1.4543... Generator Loss: 0.6171
Epoch 1/2... Discriminator Loss: 1.2559... Generator Loss: 1.6820
Epoch 1/2... Discriminator Loss: 1.3666... Generator Loss: 0.7124
Epoch 1/2... Discriminator Loss: 1.2575... Generator Loss: 1.3201
Epoch 1/2... Discriminator Loss: 1.2067... Generator Loss: 0.9685
Epoch 1/2... Discriminator Loss: 1.2254... Generator Loss: 1.4368
Epoch 1/2... Discriminator Loss: 1.4871... Generator Loss: 1.8829
Epoch 1/2... Discriminator Loss: 1.2854... Generator Loss: 0.6630
Epoch 1/2... Discriminator Loss: 1.2803... Generator Loss: 0.7046
Epoch 1/2... Discriminator Loss: 1.2217... Generator Loss: 0.8067
Epoch 1/2... Discriminator Loss: 1.2976... Generator Loss: 1.3918
Epoch 1/2... Discriminator Loss: 1.2717... Generator Loss: 0.9114
Epoch 1/2... Discriminator Loss: 1.2420... Generator Loss: 0.7640
Epoch 1/2... Discriminator Loss: 1.4652... Generator Loss: 1.8688
Epoch 1/2... Discriminator Loss: 1.1942... Generator Loss: 0.7554
Epoch 1/2... Discriminator Loss: 1.1601... Generator Loss: 0.9079
Epoch 1/2... Discriminator Loss: 1.1490... Generator Loss: 1.0015
Epoch 1/2... Discriminator Loss: 1.1628... Generator Loss: 0.8067
Epoch 1/2... Discriminator Loss: 1.1804... Generator Loss: 0.9854
Epoch 1/2... Discriminator Loss: 1.1186... Generator Loss: 1.1019
Epoch 1/2... Discriminator Loss: 1.2651... Generator Loss: 0.6206
Epoch 1/2... Discriminator Loss: 1.1391... Generator Loss: 1.0169
Epoch 1/2... Discriminator Loss: 1.1064... Generator Loss: 0.8685
Epoch 1/2... Discriminator Loss: 1.1281... Generator Loss: 0.9917
Epoch 1/2... Discriminator Loss: 1.1437... Generator Loss: 0.7836
Epoch 1/2... Discriminator Loss: 1.2740... Generator Loss: 0.6611
Epoch 1/2... Discriminator Loss: 1.3291... Generator Loss: 0.5293
Epoch 1/2... Discriminator Loss: 1.5081... Generator Loss: 0.4450
Epoch 1/2... Discriminator Loss: 0.9958... Generator Loss: 1.3607
Epoch 1/2... Discriminator Loss: 1.4878... Generator Loss: 2.2292
Epoch 1/2... Discriminator Loss: 1.0508... Generator Loss: 1.1508
Epoch 1/2... Discriminator Loss: 1.3890... Generator Loss: 0.8106
Epoch 1/2... Discriminator Loss: 1.1674... Generator Loss: 1.0226
Epoch 1/2... Discriminator Loss: 1.3133... Generator Loss: 1.6591
Epoch 1/2... Discriminator Loss: 1.1954... Generator Loss: 0.8411
Epoch 1/2... Discriminator Loss: 1.1094... Generator Loss: 1.4938
Epoch 1/2... Discriminator Loss: 1.0470... Generator Loss: 1.4884
Epoch 1/2... Discriminator Loss: 1.0178... Generator Loss: 1.6878
Epoch 1/2... Discriminator Loss: 1.8663... Generator Loss: 2.8729
Epoch 1/2... Discriminator Loss: 1.1665... Generator Loss: 0.7215
Epoch 1/2... Discriminator Loss: 1.4242... Generator Loss: 0.5058
Epoch 1/2... Discriminator Loss: 1.1615... Generator Loss: 1.0423
Epoch 1/2... Discriminator Loss: 1.1138... Generator Loss: 0.9564
Epoch 1/2... Discriminator Loss: 1.2724... Generator Loss: 0.6068
Epoch 1/2... Discriminator Loss: 1.1517... Generator Loss: 0.6879
Epoch 2/2... Discriminator Loss: 1.1069... Generator Loss: 0.8198
Epoch 2/2... Discriminator Loss: 1.0055... Generator Loss: 1.2589
Epoch 2/2... Discriminator Loss: 0.8499... Generator Loss: 1.1748
Epoch 2/2... Discriminator Loss: 1.1103... Generator Loss: 1.0716
Epoch 2/2... Discriminator Loss: 0.9565... Generator Loss: 1.0247
Epoch 2/2... Discriminator Loss: 1.0258... Generator Loss: 0.8859
Epoch 2/2... Discriminator Loss: 1.0614... Generator Loss: 0.8500
Epoch 2/2... Discriminator Loss: 0.9778... Generator Loss: 0.8581
Epoch 2/2... Discriminator Loss: 1.1464... Generator Loss: 0.7488
Epoch 2/2... Discriminator Loss: 0.8498... Generator Loss: 1.3610
Epoch 2/2... Discriminator Loss: 1.2762... Generator Loss: 0.6073
Epoch 2/2... Discriminator Loss: 1.0273... Generator Loss: 2.1706
Epoch 2/2... Discriminator Loss: 0.9757... Generator Loss: 0.8700
Epoch 2/2... Discriminator Loss: 0.7081... Generator Loss: 1.6366
Epoch 2/2... Discriminator Loss: 0.9533... Generator Loss: 0.9006
Epoch 2/2... Discriminator Loss: 0.8210... Generator Loss: 1.1194
Epoch 2/2... Discriminator Loss: 1.0993... Generator Loss: 2.4129
Epoch 2/2... Discriminator Loss: 0.9302... Generator Loss: 1.0453
Epoch 2/2... Discriminator Loss: 1.0163... Generator Loss: 1.0332
Epoch 2/2... Discriminator Loss: 0.9024... Generator Loss: 1.1570
Epoch 2/2... Discriminator Loss: 0.7866... Generator Loss: 1.5080
Epoch 2/2... Discriminator Loss: 1.0054... Generator Loss: 0.9984
Epoch 2/2... Discriminator Loss: 0.7198... Generator Loss: 1.4758
Epoch 2/2... Discriminator Loss: 1.2945... Generator Loss: 2.3272
Epoch 2/2... Discriminator Loss: 0.7339... Generator Loss: 1.6988
Epoch 2/2... Discriminator Loss: 1.0659... Generator Loss: 1.2033
Epoch 2/2... Discriminator Loss: 0.7443... Generator Loss: 1.4620
Epoch 2/2... Discriminator Loss: 1.7458... Generator Loss: 3.5460
Epoch 2/2... Discriminator Loss: 0.5912... Generator Loss: 1.7545
Epoch 2/2... Discriminator Loss: 0.7770... Generator Loss: 1.2696
Epoch 2/2... Discriminator Loss: 1.0250... Generator Loss: 0.9692
Epoch 2/2... Discriminator Loss: 0.9140... Generator Loss: 1.0755
Epoch 2/2... Discriminator Loss: 0.6818... Generator Loss: 1.6907
Epoch 2/2... Discriminator Loss: 1.0113... Generator Loss: 2.4626
Epoch 2/2... Discriminator Loss: 0.7627... Generator Loss: 1.1144
Epoch 2/2... Discriminator Loss: 0.6692... Generator Loss: 2.3676
Epoch 2/2... Discriminator Loss: 0.6063... Generator Loss: 1.9428
Epoch 2/2... Discriminator Loss: 0.6383... Generator Loss: 2.2505
Epoch 2/2... Discriminator Loss: 0.6944... Generator Loss: 2.0365
Epoch 2/2... Discriminator Loss: 1.0067... Generator Loss: 1.0239
Epoch 2/2... Discriminator Loss: 1.8986... Generator Loss: 4.6471
Epoch 2/2... Discriminator Loss: 0.7868... Generator Loss: 2.0584
Epoch 2/2... Discriminator Loss: 1.0879... Generator Loss: 0.8642
Epoch 2/2... Discriminator Loss: 0.7942... Generator Loss: 1.3791
Epoch 2/2... Discriminator Loss: 0.6206... Generator Loss: 1.7914
Epoch 2/2... Discriminator Loss: 1.5652... Generator Loss: 0.6049
Epoch 2/2... Discriminator Loss: 0.7473... Generator Loss: 1.4162

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [30]:
batch_size = 32
z_dim = 100
learning_rate = 1e-3
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 3.3227... Generator Loss: 3.4466
Epoch 1/1... Discriminator Loss: 1.2825... Generator Loss: 1.1458
Epoch 1/1... Discriminator Loss: 1.2528... Generator Loss: 0.9013
Epoch 1/1... Discriminator Loss: 1.5446... Generator Loss: 0.5605
Epoch 1/1... Discriminator Loss: 1.2746... Generator Loss: 1.7025
Epoch 1/1... Discriminator Loss: 1.1740... Generator Loss: 1.1645
Epoch 1/1... Discriminator Loss: 2.8535... Generator Loss: 4.3134
Epoch 1/1... Discriminator Loss: 1.8538... Generator Loss: 0.4878
Epoch 1/1... Discriminator Loss: 1.1219... Generator Loss: 0.8755
Epoch 1/1... Discriminator Loss: 0.9179... Generator Loss: 1.1862
Epoch 1/1... Discriminator Loss: 1.1498... Generator Loss: 1.0701
Epoch 1/1... Discriminator Loss: 1.3627... Generator Loss: 0.6196
Epoch 1/1... Discriminator Loss: 1.2302... Generator Loss: 0.9419
Epoch 1/1... Discriminator Loss: 1.3352... Generator Loss: 0.8161
Epoch 1/1... Discriminator Loss: 1.2693... Generator Loss: 1.1323
Epoch 1/1... Discriminator Loss: 1.0881... Generator Loss: 0.9346
Epoch 1/1... Discriminator Loss: 1.5889... Generator Loss: 0.7167
Epoch 1/1... Discriminator Loss: 1.8168... Generator Loss: 0.3525
Epoch 1/1... Discriminator Loss: 1.2466... Generator Loss: 1.0784
Epoch 1/1... Discriminator Loss: 1.3458... Generator Loss: 1.3810
Epoch 1/1... Discriminator Loss: 1.6326... Generator Loss: 0.4125
Epoch 1/1... Discriminator Loss: 1.5514... Generator Loss: 0.7522
Epoch 1/1... Discriminator Loss: 1.3498... Generator Loss: 0.9883
Epoch 1/1... Discriminator Loss: 1.4956... Generator Loss: 0.6255
Epoch 1/1... Discriminator Loss: 1.4659... Generator Loss: 0.9323
Epoch 1/1... Discriminator Loss: 1.2216... Generator Loss: 0.9518
Epoch 1/1... Discriminator Loss: 1.3919... Generator Loss: 0.6899
Epoch 1/1... Discriminator Loss: 1.3078... Generator Loss: 0.6995
Epoch 1/1... Discriminator Loss: 1.2094... Generator Loss: 1.2212
Epoch 1/1... Discriminator Loss: 1.8704... Generator Loss: 0.4291
Epoch 1/1... Discriminator Loss: 1.2049... Generator Loss: 0.8439
Epoch 1/1... Discriminator Loss: 1.6247... Generator Loss: 0.7743
Epoch 1/1... Discriminator Loss: 1.5471... Generator Loss: 0.7712
Epoch 1/1... Discriminator Loss: 1.3203... Generator Loss: 0.9339
Epoch 1/1... Discriminator Loss: 1.2660... Generator Loss: 1.0225
Epoch 1/1... Discriminator Loss: 1.3789... Generator Loss: 1.1313
Epoch 1/1... Discriminator Loss: 1.3476... Generator Loss: 0.6986
Epoch 1/1... Discriminator Loss: 1.6321... Generator Loss: 0.6397
Epoch 1/1... Discriminator Loss: 1.7320... Generator Loss: 0.4251
Epoch 1/1... Discriminator Loss: 1.4153... Generator Loss: 0.9791
Epoch 1/1... Discriminator Loss: 1.5643... Generator Loss: 1.4081
Epoch 1/1... Discriminator Loss: 1.4285... Generator Loss: 0.9046
Epoch 1/1... Discriminator Loss: 1.4142... Generator Loss: 0.6871
Epoch 1/1... Discriminator Loss: 1.3266... Generator Loss: 0.7772
Epoch 1/1... Discriminator Loss: 1.4432... Generator Loss: 0.6775
Epoch 1/1... Discriminator Loss: 1.4550... Generator Loss: 1.1108
Epoch 1/1... Discriminator Loss: 1.3721... Generator Loss: 0.8997
Epoch 1/1... Discriminator Loss: 1.3806... Generator Loss: 0.9502
Epoch 1/1... Discriminator Loss: 1.5078... Generator Loss: 0.5991
Epoch 1/1... Discriminator Loss: 1.2628... Generator Loss: 0.7623
Epoch 1/1... Discriminator Loss: 1.2634... Generator Loss: 0.7595
Epoch 1/1... Discriminator Loss: 1.2513... Generator Loss: 0.9437
Epoch 1/1... Discriminator Loss: 1.3382... Generator Loss: 0.8324
Epoch 1/1... Discriminator Loss: 1.3342... Generator Loss: 0.9438
Epoch 1/1... Discriminator Loss: 1.3371... Generator Loss: 0.7127
Epoch 1/1... Discriminator Loss: 1.3378... Generator Loss: 0.8212
Epoch 1/1... Discriminator Loss: 1.3636... Generator Loss: 1.0267
Epoch 1/1... Discriminator Loss: 1.3513... Generator Loss: 0.8027
Epoch 1/1... Discriminator Loss: 1.5666... Generator Loss: 0.7737
Epoch 1/1... Discriminator Loss: 1.3243... Generator Loss: 0.8074
Epoch 1/1... Discriminator Loss: 1.3399... Generator Loss: 0.7670
Epoch 1/1... Discriminator Loss: 1.4572... Generator Loss: 0.8214
Epoch 1/1... Discriminator Loss: 1.2741... Generator Loss: 0.7859
Epoch 1/1... Discriminator Loss: 1.4062... Generator Loss: 0.6914
Epoch 1/1... Discriminator Loss: 1.2064... Generator Loss: 0.8629
Epoch 1/1... Discriminator Loss: 1.2470... Generator Loss: 0.8366
Epoch 1/1... Discriminator Loss: 1.4058... Generator Loss: 0.6448
Epoch 1/1... Discriminator Loss: 1.3077... Generator Loss: 0.7556
Epoch 1/1... Discriminator Loss: 1.2650... Generator Loss: 0.6834
Epoch 1/1... Discriminator Loss: 1.3379... Generator Loss: 0.7839
Epoch 1/1... Discriminator Loss: 1.2904... Generator Loss: 0.8707
Epoch 1/1... Discriminator Loss: 1.3322... Generator Loss: 0.8178
Epoch 1/1... Discriminator Loss: 1.6286... Generator Loss: 0.6684
Epoch 1/1... Discriminator Loss: 1.3640... Generator Loss: 0.9265
Epoch 1/1... Discriminator Loss: 1.3172... Generator Loss: 0.8213
Epoch 1/1... Discriminator Loss: 1.3359... Generator Loss: 0.9236
Epoch 1/1... Discriminator Loss: 1.4554... Generator Loss: 0.6370
Epoch 1/1... Discriminator Loss: 1.2818... Generator Loss: 0.7790
Epoch 1/1... Discriminator Loss: 1.3616... Generator Loss: 0.7261
Epoch 1/1... Discriminator Loss: 1.4390... Generator Loss: 0.4688
Epoch 1/1... Discriminator Loss: 1.5323... Generator Loss: 0.7908
Epoch 1/1... Discriminator Loss: 1.3987... Generator Loss: 0.9183
Epoch 1/1... Discriminator Loss: 1.3892... Generator Loss: 0.8691
Epoch 1/1... Discriminator Loss: 1.4214... Generator Loss: 0.8215
Epoch 1/1... Discriminator Loss: 1.2998... Generator Loss: 1.1356
Epoch 1/1... Discriminator Loss: 1.2906... Generator Loss: 0.6946
Epoch 1/1... Discriminator Loss: 1.4240... Generator Loss: 0.7742
Epoch 1/1... Discriminator Loss: 1.2562... Generator Loss: 0.9009
Epoch 1/1... Discriminator Loss: 1.3580... Generator Loss: 0.7732
Epoch 1/1... Discriminator Loss: 1.3387... Generator Loss: 0.8922
Epoch 1/1... Discriminator Loss: 1.4013... Generator Loss: 0.8486
Epoch 1/1... Discriminator Loss: 1.4215... Generator Loss: 0.6704
Epoch 1/1... Discriminator Loss: 1.2438... Generator Loss: 0.8041
Epoch 1/1... Discriminator Loss: 1.4403... Generator Loss: 0.6008
Epoch 1/1... Discriminator Loss: 1.1467... Generator Loss: 0.9156
Epoch 1/1... Discriminator Loss: 1.3982... Generator Loss: 0.8498
Epoch 1/1... Discriminator Loss: 1.3912... Generator Loss: 0.6588
Epoch 1/1... Discriminator Loss: 1.3506... Generator Loss: 0.9062
Epoch 1/1... Discriminator Loss: 1.4680... Generator Loss: 0.5910
Epoch 1/1... Discriminator Loss: 1.3490... Generator Loss: 0.9067
Epoch 1/1... Discriminator Loss: 1.4123... Generator Loss: 0.7646
Epoch 1/1... Discriminator Loss: 1.3154... Generator Loss: 0.7744
Epoch 1/1... Discriminator Loss: 1.3431... Generator Loss: 0.7761
Epoch 1/1... Discriminator Loss: 1.3738... Generator Loss: 0.7886
Epoch 1/1... Discriminator Loss: 1.3797... Generator Loss: 0.7794
Epoch 1/1... Discriminator Loss: 1.2846... Generator Loss: 0.7225
Epoch 1/1... Discriminator Loss: 1.3093... Generator Loss: 0.8229
Epoch 1/1... Discriminator Loss: 1.2809... Generator Loss: 0.8127
Epoch 1/1... Discriminator Loss: 1.3782... Generator Loss: 0.7338
Epoch 1/1... Discriminator Loss: 1.3408... Generator Loss: 0.7959
Epoch 1/1... Discriminator Loss: 1.1656... Generator Loss: 0.9135
Epoch 1/1... Discriminator Loss: 1.2513... Generator Loss: 1.0250
Epoch 1/1... Discriminator Loss: 1.2561... Generator Loss: 0.7523
Epoch 1/1... Discriminator Loss: 1.3670... Generator Loss: 0.8443
Epoch 1/1... Discriminator Loss: 1.4544... Generator Loss: 0.8676
Epoch 1/1... Discriminator Loss: 1.3653... Generator Loss: 0.6431
Epoch 1/1... Discriminator Loss: 1.3072... Generator Loss: 0.9469
Epoch 1/1... Discriminator Loss: 1.5620... Generator Loss: 0.5900
Epoch 1/1... Discriminator Loss: 1.4003... Generator Loss: 0.9052
Epoch 1/1... Discriminator Loss: 1.4074... Generator Loss: 0.5779
Epoch 1/1... Discriminator Loss: 1.2824... Generator Loss: 0.9696
Epoch 1/1... Discriminator Loss: 1.2711... Generator Loss: 0.8616
Epoch 1/1... Discriminator Loss: 1.2723... Generator Loss: 0.8806
Epoch 1/1... Discriminator Loss: 1.3565... Generator Loss: 0.8031
Epoch 1/1... Discriminator Loss: 1.3406... Generator Loss: 0.9350
Epoch 1/1... Discriminator Loss: 1.2507... Generator Loss: 0.8835
Epoch 1/1... Discriminator Loss: 1.2892... Generator Loss: 0.7399
Epoch 1/1... Discriminator Loss: 1.3857... Generator Loss: 0.8757
Epoch 1/1... Discriminator Loss: 1.3532... Generator Loss: 1.0301
Epoch 1/1... Discriminator Loss: 1.2849... Generator Loss: 0.8324
Epoch 1/1... Discriminator Loss: 1.2735... Generator Loss: 0.8620
Epoch 1/1... Discriminator Loss: 1.4089... Generator Loss: 0.6032
Epoch 1/1... Discriminator Loss: 1.2169... Generator Loss: 0.8949
Epoch 1/1... Discriminator Loss: 1.3315... Generator Loss: 0.7402
Epoch 1/1... Discriminator Loss: 1.2996... Generator Loss: 0.8023
Epoch 1/1... Discriminator Loss: 1.3586... Generator Loss: 0.9793
Epoch 1/1... Discriminator Loss: 1.2253... Generator Loss: 0.7753
Epoch 1/1... Discriminator Loss: 1.3351... Generator Loss: 1.2596
Epoch 1/1... Discriminator Loss: 1.2254... Generator Loss: 0.8405
Epoch 1/1... Discriminator Loss: 1.4256... Generator Loss: 0.7796
Epoch 1/1... Discriminator Loss: 1.2700... Generator Loss: 0.9706
Epoch 1/1... Discriminator Loss: 1.2664... Generator Loss: 0.8403
Epoch 1/1... Discriminator Loss: 1.3811... Generator Loss: 0.7540
Epoch 1/1... Discriminator Loss: 1.2840... Generator Loss: 0.7868
Epoch 1/1... Discriminator Loss: 1.2172... Generator Loss: 0.7646
Epoch 1/1... Discriminator Loss: 1.3335... Generator Loss: 0.6836
Epoch 1/1... Discriminator Loss: 1.3265... Generator Loss: 0.9191
Epoch 1/1... Discriminator Loss: 1.3735... Generator Loss: 0.7795
Epoch 1/1... Discriminator Loss: 1.2683... Generator Loss: 1.0138
Epoch 1/1... Discriminator Loss: 1.3931... Generator Loss: 0.7740
Epoch 1/1... Discriminator Loss: 1.3667... Generator Loss: 0.9033
Epoch 1/1... Discriminator Loss: 1.1902... Generator Loss: 0.9400
Epoch 1/1... Discriminator Loss: 1.4823... Generator Loss: 0.7614
Epoch 1/1... Discriminator Loss: 1.1976... Generator Loss: 0.9464
Epoch 1/1... Discriminator Loss: 1.3756... Generator Loss: 0.7191
Epoch 1/1... Discriminator Loss: 1.4381... Generator Loss: 0.7883
Epoch 1/1... Discriminator Loss: 1.3407... Generator Loss: 0.8947
Epoch 1/1... Discriminator Loss: 1.3657... Generator Loss: 0.8405
Epoch 1/1... Discriminator Loss: 1.3214... Generator Loss: 0.9493
Epoch 1/1... Discriminator Loss: 1.2825... Generator Loss: 0.9826
Epoch 1/1... Discriminator Loss: 1.3529... Generator Loss: 0.8357
Epoch 1/1... Discriminator Loss: 1.4279... Generator Loss: 0.7431
Epoch 1/1... Discriminator Loss: 1.3168... Generator Loss: 0.9112
Epoch 1/1... Discriminator Loss: 1.4322... Generator Loss: 0.8233
Epoch 1/1... Discriminator Loss: 1.4792... Generator Loss: 0.7284
Epoch 1/1... Discriminator Loss: 1.4359... Generator Loss: 0.7332
Epoch 1/1... Discriminator Loss: 1.2730... Generator Loss: 0.8444
Epoch 1/1... Discriminator Loss: 1.3669... Generator Loss: 0.9057
Epoch 1/1... Discriminator Loss: 1.3801... Generator Loss: 0.7836
Epoch 1/1... Discriminator Loss: 1.3152... Generator Loss: 0.8475
Epoch 1/1... Discriminator Loss: 1.3714... Generator Loss: 0.6574
Epoch 1/1... Discriminator Loss: 1.3567... Generator Loss: 0.8165
Epoch 1/1... Discriminator Loss: 1.3002... Generator Loss: 0.8110
Epoch 1/1... Discriminator Loss: 1.2201... Generator Loss: 1.0035
Epoch 1/1... Discriminator Loss: 1.3907... Generator Loss: 0.8290
Epoch 1/1... Discriminator Loss: 1.2766... Generator Loss: 0.9217
Epoch 1/1... Discriminator Loss: 1.3320... Generator Loss: 0.7962
Epoch 1/1... Discriminator Loss: 1.3776... Generator Loss: 0.7668
Epoch 1/1... Discriminator Loss: 1.3204... Generator Loss: 0.8698
Epoch 1/1... Discriminator Loss: 1.2529... Generator Loss: 0.9062
Epoch 1/1... Discriminator Loss: 1.3365... Generator Loss: 0.7406
Epoch 1/1... Discriminator Loss: 1.3937... Generator Loss: 0.7836
Epoch 1/1... Discriminator Loss: 1.3062... Generator Loss: 0.7334
Epoch 1/1... Discriminator Loss: 1.2777... Generator Loss: 0.9693
Epoch 1/1... Discriminator Loss: 1.3979... Generator Loss: 0.9124
Epoch 1/1... Discriminator Loss: 1.3859... Generator Loss: 0.8627
Epoch 1/1... Discriminator Loss: 1.3137... Generator Loss: 0.8204
Epoch 1/1... Discriminator Loss: 1.2710... Generator Loss: 0.7475
Epoch 1/1... Discriminator Loss: 1.2927... Generator Loss: 0.7478
Epoch 1/1... Discriminator Loss: 1.3145... Generator Loss: 0.7698
Epoch 1/1... Discriminator Loss: 1.3099... Generator Loss: 1.0282
Epoch 1/1... Discriminator Loss: 1.2482... Generator Loss: 0.7670
Epoch 1/1... Discriminator Loss: 1.4885... Generator Loss: 0.7534
Epoch 1/1... Discriminator Loss: 1.4157... Generator Loss: 0.7484
Epoch 1/1... Discriminator Loss: 1.3410... Generator Loss: 0.7770
Epoch 1/1... Discriminator Loss: 1.4606... Generator Loss: 0.6544
Epoch 1/1... Discriminator Loss: 1.4432... Generator Loss: 0.8481
Epoch 1/1... Discriminator Loss: 1.3136... Generator Loss: 1.0316
Epoch 1/1... Discriminator Loss: 1.2902... Generator Loss: 0.7984
Epoch 1/1... Discriminator Loss: 1.2719... Generator Loss: 0.7769
Epoch 1/1... Discriminator Loss: 1.4120... Generator Loss: 0.8274
Epoch 1/1... Discriminator Loss: 1.3358... Generator Loss: 0.7416
Epoch 1/1... Discriminator Loss: 1.3842... Generator Loss: 0.7331
Epoch 1/1... Discriminator Loss: 1.2074... Generator Loss: 0.9052
Epoch 1/1... Discriminator Loss: 1.4476... Generator Loss: 1.0195
Epoch 1/1... Discriminator Loss: 1.3335... Generator Loss: 1.0994
Epoch 1/1... Discriminator Loss: 1.3715... Generator Loss: 0.7251
Epoch 1/1... Discriminator Loss: 1.3544... Generator Loss: 0.7317
Epoch 1/1... Discriminator Loss: 1.2523... Generator Loss: 0.7888
Epoch 1/1... Discriminator Loss: 1.2229... Generator Loss: 0.9994
Epoch 1/1... Discriminator Loss: 1.2692... Generator Loss: 0.7943
Epoch 1/1... Discriminator Loss: 1.3175... Generator Loss: 1.0019
Epoch 1/1... Discriminator Loss: 1.3196... Generator Loss: 1.0786
Epoch 1/1... Discriminator Loss: 1.2468... Generator Loss: 0.7825
Epoch 1/1... Discriminator Loss: 1.4158... Generator Loss: 0.7565
Epoch 1/1... Discriminator Loss: 1.3321... Generator Loss: 0.9167
Epoch 1/1... Discriminator Loss: 1.3208... Generator Loss: 0.7109
Epoch 1/1... Discriminator Loss: 1.4208... Generator Loss: 0.7856
Epoch 1/1... Discriminator Loss: 1.3051... Generator Loss: 0.6746
Epoch 1/1... Discriminator Loss: 1.2817... Generator Loss: 0.8355
Epoch 1/1... Discriminator Loss: 1.2547... Generator Loss: 0.9353
Epoch 1/1... Discriminator Loss: 1.3160... Generator Loss: 0.8914
Epoch 1/1... Discriminator Loss: 1.3025... Generator Loss: 0.7860
Epoch 1/1... Discriminator Loss: 1.3142... Generator Loss: 0.9489
Epoch 1/1... Discriminator Loss: 1.3483... Generator Loss: 0.5652
Epoch 1/1... Discriminator Loss: 1.3289... Generator Loss: 0.8061
Epoch 1/1... Discriminator Loss: 1.2941... Generator Loss: 0.7825
Epoch 1/1... Discriminator Loss: 1.2662... Generator Loss: 0.9741
Epoch 1/1... Discriminator Loss: 1.3865... Generator Loss: 0.6954
Epoch 1/1... Discriminator Loss: 1.3266... Generator Loss: 0.7111
Epoch 1/1... Discriminator Loss: 1.3017... Generator Loss: 0.8463
Epoch 1/1... Discriminator Loss: 1.2752... Generator Loss: 0.8969
Epoch 1/1... Discriminator Loss: 1.3188... Generator Loss: 0.7184
Epoch 1/1... Discriminator Loss: 1.2814... Generator Loss: 0.8740
Epoch 1/1... Discriminator Loss: 1.3529... Generator Loss: 0.7919
Epoch 1/1... Discriminator Loss: 1.3267... Generator Loss: 0.8045
Epoch 1/1... Discriminator Loss: 1.4871... Generator Loss: 0.6446
Epoch 1/1... Discriminator Loss: 1.4772... Generator Loss: 0.5392
Epoch 1/1... Discriminator Loss: 1.4234... Generator Loss: 0.8296
Epoch 1/1... Discriminator Loss: 1.2509... Generator Loss: 1.0638
Epoch 1/1... Discriminator Loss: 1.2944... Generator Loss: 0.8675
Epoch 1/1... Discriminator Loss: 1.1513... Generator Loss: 1.0418
Epoch 1/1... Discriminator Loss: 1.3895... Generator Loss: 0.8636
Epoch 1/1... Discriminator Loss: 1.3673... Generator Loss: 0.7027
Epoch 1/1... Discriminator Loss: 1.3424... Generator Loss: 0.9097
Epoch 1/1... Discriminator Loss: 1.2821... Generator Loss: 0.7767
Epoch 1/1... Discriminator Loss: 1.3382... Generator Loss: 0.8110
Epoch 1/1... Discriminator Loss: 1.2764... Generator Loss: 0.8796
Epoch 1/1... Discriminator Loss: 1.3360... Generator Loss: 0.8628
Epoch 1/1... Discriminator Loss: 1.3480... Generator Loss: 0.8937
Epoch 1/1... Discriminator Loss: 1.3415... Generator Loss: 0.7552
Epoch 1/1... Discriminator Loss: 1.4135... Generator Loss: 0.8064
Epoch 1/1... Discriminator Loss: 1.2789... Generator Loss: 0.7011
Epoch 1/1... Discriminator Loss: 1.3597... Generator Loss: 0.7402
Epoch 1/1... Discriminator Loss: 1.2994... Generator Loss: 0.8110
Epoch 1/1... Discriminator Loss: 1.3931... Generator Loss: 0.8702
Epoch 1/1... Discriminator Loss: 1.1964... Generator Loss: 0.7673
Epoch 1/1... Discriminator Loss: 1.3718... Generator Loss: 0.7808
Epoch 1/1... Discriminator Loss: 1.2663... Generator Loss: 0.6647
Epoch 1/1... Discriminator Loss: 1.3211... Generator Loss: 0.8110
Epoch 1/1... Discriminator Loss: 1.3957... Generator Loss: 0.7496
Epoch 1/1... Discriminator Loss: 1.3499... Generator Loss: 0.8058
Epoch 1/1... Discriminator Loss: 1.4671... Generator Loss: 0.6556
Epoch 1/1... Discriminator Loss: 1.2349... Generator Loss: 0.8091
Epoch 1/1... Discriminator Loss: 1.3761... Generator Loss: 0.7740
Epoch 1/1... Discriminator Loss: 1.3272... Generator Loss: 0.7403
Epoch 1/1... Discriminator Loss: 1.3061... Generator Loss: 0.6980
Epoch 1/1... Discriminator Loss: 1.3562... Generator Loss: 0.9828
Epoch 1/1... Discriminator Loss: 1.3261... Generator Loss: 0.9024
Epoch 1/1... Discriminator Loss: 1.3548... Generator Loss: 0.7002
Epoch 1/1... Discriminator Loss: 1.2940... Generator Loss: 0.9425
Epoch 1/1... Discriminator Loss: 1.1930... Generator Loss: 0.8910
Epoch 1/1... Discriminator Loss: 1.3240... Generator Loss: 0.6698
Epoch 1/1... Discriminator Loss: 1.1767... Generator Loss: 0.9417
Epoch 1/1... Discriminator Loss: 1.5094... Generator Loss: 0.7643
Epoch 1/1... Discriminator Loss: 1.3173... Generator Loss: 0.7926
Epoch 1/1... Discriminator Loss: 1.3070... Generator Loss: 0.6324
Epoch 1/1... Discriminator Loss: 1.3458... Generator Loss: 0.8861
Epoch 1/1... Discriminator Loss: 1.3205... Generator Loss: 0.7464
Epoch 1/1... Discriminator Loss: 1.2115... Generator Loss: 0.9308
Epoch 1/1... Discriminator Loss: 1.2153... Generator Loss: 0.9279
Epoch 1/1... Discriminator Loss: 1.4102... Generator Loss: 0.6887
Epoch 1/1... Discriminator Loss: 1.2776... Generator Loss: 0.7858
Epoch 1/1... Discriminator Loss: 1.3933... Generator Loss: 0.7303
Epoch 1/1... Discriminator Loss: 1.3741... Generator Loss: 0.7849
Epoch 1/1... Discriminator Loss: 1.4420... Generator Loss: 0.8177
Epoch 1/1... Discriminator Loss: 1.3166... Generator Loss: 0.8537
Epoch 1/1... Discriminator Loss: 1.2609... Generator Loss: 0.8677
Epoch 1/1... Discriminator Loss: 1.2660... Generator Loss: 0.7193
Epoch 1/1... Discriminator Loss: 1.3077... Generator Loss: 0.7254
Epoch 1/1... Discriminator Loss: 1.2581... Generator Loss: 0.8179
Epoch 1/1... Discriminator Loss: 1.5074... Generator Loss: 0.7897
Epoch 1/1... Discriminator Loss: 1.3750... Generator Loss: 0.6835
Epoch 1/1... Discriminator Loss: 1.3463... Generator Loss: 0.7249
Epoch 1/1... Discriminator Loss: 1.4267... Generator Loss: 0.6641
Epoch 1/1... Discriminator Loss: 1.2319... Generator Loss: 0.9012
Epoch 1/1... Discriminator Loss: 1.3580... Generator Loss: 0.6833
Epoch 1/1... Discriminator Loss: 1.3401... Generator Loss: 0.7118
Epoch 1/1... Discriminator Loss: 1.2623... Generator Loss: 0.7609
Epoch 1/1... Discriminator Loss: 1.3042... Generator Loss: 0.9282
Epoch 1/1... Discriminator Loss: 1.1313... Generator Loss: 0.8652
Epoch 1/1... Discriminator Loss: 1.4706... Generator Loss: 1.2030
Epoch 1/1... Discriminator Loss: 1.3633... Generator Loss: 1.0422
Epoch 1/1... Discriminator Loss: 1.3150... Generator Loss: 0.8198
Epoch 1/1... Discriminator Loss: 1.2593... Generator Loss: 0.8320
Epoch 1/1... Discriminator Loss: 1.2235... Generator Loss: 0.9473
Epoch 1/1... Discriminator Loss: 1.3042... Generator Loss: 0.7031
Epoch 1/1... Discriminator Loss: 1.2276... Generator Loss: 0.8731
Epoch 1/1... Discriminator Loss: 1.3646... Generator Loss: 0.7780
Epoch 1/1... Discriminator Loss: 1.2728... Generator Loss: 0.8178
Epoch 1/1... Discriminator Loss: 1.2640... Generator Loss: 0.8278
Epoch 1/1... Discriminator Loss: 1.2664... Generator Loss: 0.9559
Epoch 1/1... Discriminator Loss: 1.3280... Generator Loss: 0.7221
Epoch 1/1... Discriminator Loss: 1.1743... Generator Loss: 0.9459
Epoch 1/1... Discriminator Loss: 1.4139... Generator Loss: 0.6434
Epoch 1/1... Discriminator Loss: 1.3489... Generator Loss: 0.7317

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.